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2020 through 2023 were unusually tumultuous years for children in the United States, and children's welfare was prominent in political debate. Theories in moral psychology suggest that political parties would treat concerns for children using different moral frames, and that moral conflict might drive substantial polarization in discussions about children. However, such partisan frames may still differ very little if there is limited underlying disagreement about moral issues and everyday concerns in childhood when not explicitly referencing politics. We evaluate claims of universality and division in moral language using tweets from 2019-2023 linked to U.S. voter records, focusing on expressed morality. Our results show that mentions of children by Republicans and Democrats are usually similar, differing no more than mentions by women and men, and tend to contain no large differences in accompanying moral words. To the extent that mentions of children did differ across parties, these differences were constrained to topics polarized well before the pandemic -- and slightly heightened when co-mentioned with `kids' or `children'. These topics reflected a small fraction of conversations about children. Overall, polarization of online discussion around childhood appears to reflect escalated polarization on lines of existing partisan conflicts rather than concerns originating from new concerns about the welfare of children during and after the pandemic., Comment: Accepted for publication in the International AAAI Conference on Web and Social Media (ICWSM) 2025

The Earth system is accumulating energy due to human-induced activities. More than 90 percent of this energy has been stored in the ocean as heat since 1970, with about 64 percent of that in the upper 700 m. Differences in upper ocean heat content anomaly (OHCA) estimates, however, exist. Here, we evaluate spread in upper OHCA estimates arising from choices in instrumental bias corrections and mapping methods, in addition to the effect of using a common ocean mask. The same dataset was mapped by six research groups for 1970 to 2008, with six instrumental bias corrections applied to expendable bathythermograph (XBT) data. We find that use of a common ocean mask may impact estimation of global OHCA by +- 13 percent. Uncertainty due to mapping method dominates over XBT bias correction at a global scale and is largest in the Indian Ocean and in the eddy-rich and frontal regions of all basins. Uncertainty due to XBT bias correction is largest in the Pacific Ocean within 30N to 30S. In both mapping and XBT cases, spread is higher since the 1990s. Important differences in spatial trends among mapping methods are found in the well-observed Northwest Atlantic and the poorly-observed Southern Ocean. Although our results cannot identify the best mapping or bias correction schemes, they identify where and when greater uncertainties exist, and so where further refinements may yield the largest improvements. Our results highlight the need for a future international coordination to evaluate performance of existing mapping methods., Comment: Submitted to AMS Journal of Climate (30 July 2020). Feedback welcome. Please email: abhishek.abhisheksavita@utas.edu.au

Manual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook. In particular, scholars often choose what information to give to annotators without examining these decisions empirically. For subjective tasks such as sentiment analysis, sarcasm, and stance detection, such choices can impact results. Here, for the task of political stance detection on Twitter, we show that providing too little context can result in noisy and uncertain annotations, whereas providing too strong a context may cause it to outweigh other signals. To characterize and reduce these biases, we develop ConStance, a general model for reasoning about annotations across information conditions. Given conflicting labels produced by multiple annotators seeing the same instances with different contexts, ConStance simultaneously estimates gold standard labels and also learns a classifier for new instances. We show that the classifier learned by ConStance outperforms a variety of baselines at predicting political stance, while the model's interpretable parameters shed light on the effects of each context., Comment: To appear at EMNLP 2017